Equipment can become worn over time and, eventually, fail. For example, blades in turbines may develop spalls or cracks over time, which can lead to catastrophic failure of the turbines and/or significant downtime of the turbines if the damage is not discovered sufficiently early to avoid significant repair or replacement of parts in the turbines. Some known systems and methods can visually inspect the components of equipment in order to identify damage to the equipment.
But, these systems and methods have certain faults. As one example, the characterization of the damage appearing in images or video of the equipment can be highly subjective and prone to error. As another example, some of these systems and methods rely on image background subtraction approaches, which are susceptible to non-stationary background object movements and require precise alignment.
Some systems and methods use a trained neural network to identify damage to equipment in images, but these neural networks can require training to identify the damage. This training can require input of annotated or labeled images into the neural network. These images have pixels labeled with object classes represented by the different pixels. Different object classes are associated with different objects, such as cracks, coating spalling, etc. Creation of the annotated or labeled images can be a time-consuming and expensive process for training the neural networks.